Session variability modelling for face authentication

This study examines session variability modelling for face authentication using Gaussian mixture models. Session variability modelling aims to explicitly model and suppress detrimental within-class (inter-session) variation. The authors examine two techniques to do this, inter-session variability modelling (ISV) and joint factor analysis (JFA), which were initially developed for speaker authentication. We present a self-contained description of these two techniques and demonstrate that they can be successfully applied to face authentication. In particular, they show that using ISV leads to significant error rate reductions of, on average, 26% on the challenging and publicly available databases SCface, BANCA, MOBIO and multi-PIE. Finally, the authors show that a limitation of both ISV and JFA for face authentication is that the session variability model captures and suppresses a significant portion of between-class variation.

[1]  Samy Bengio,et al.  User authentication via adapted statistical models of face images , 2006, IEEE Transactions on Signal Processing.

[2]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[3]  Sébastien Marcel,et al.  Parts-Based Face Verification Using Local Frequency Bands , 2009, ICB.

[4]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Sébastien Marcel,et al.  Bob: a free signal processing and machine learning toolbox for researchers , 2012, ACM Multimedia.

[6]  Patrick Kenny,et al.  A Study of Interspeaker Variability in Speaker Verification , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Sébastien Marcel,et al.  An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms , 2012, ECCV Workshops.

[8]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[9]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[10]  Sébastien Marcel,et al.  Inter-session variability modelling and joint factor analysis for face authentication , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[11]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[12]  Sébastien Marcel,et al.  Cross-Pollination of Normalization Techniques From Speaker to Face Authentication Using Gaussian Mixture Models , 2012, IEEE Transactions on Information Forensics and Security.

[13]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[14]  Bruce A. Draper,et al.  An introduction to the good, the bad, & the ugly face recognition challenge problem , 2011, Face and Gesture 2011.

[15]  Sridha Sridharan,et al.  Explicit modelling of session variability for speaker verification , 2008, Comput. Speech Lang..

[16]  Kuldip K. Paliwal,et al.  Fast features for face authentication under illumination direction changes , 2003, Pattern Recognit. Lett..

[17]  Yann Rodriguez,et al.  Face detection and verification using local binary patterns , 2006 .

[18]  Matti Pietikäinen,et al.  Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[19]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[20]  Sébastien Marcel,et al.  Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS , 2003, AVBPA.

[21]  Lukás Burget,et al.  Comparison of scoring methods used in speaker recognition with Joint Factor Analysis , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[22]  Enrique Argones-Rúa,et al.  Quality-Based Score Normalization for Audiovisual Person Authentication , 2008, ICIAR.

[23]  Jean-Philippe Thiran,et al.  The BANCA Database and Evaluation Protocol , 2003, AVBPA.

[24]  Valiantsina Hubeika.  BUT system description: NIST SRE 2008 , 2008 .

[25]  Patrick Kenny,et al.  Joint Factor Analysis Versus Eigenchannels in Speaker Recognition , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[26]  Sébastien Marcel,et al.  Cross-pollination of normalisation techniques from speaker to face authentication using Gaussian mixture models , 2012 .

[27]  Carmen García Mateo,et al.  Quality-Based Score Normalization for Audiovisual Person Authentication , 2008 .

[28]  Matti Pietikäinen,et al.  Pixelwise Local Binary Pattern Models of Faces Using Kernel Density Estimation , 2009, ICB.

[29]  Mislav Grgic,et al.  SCface – surveillance cameras face database , 2011, Multimedia Tools and Applications.

[30]  Tsuhan Chen,et al.  A GMM parts based face representation for improved verification through relevance adaptation , 2004, CVPR 2004.

[31]  Brian C. Lovell,et al.  Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference , 2009, ICB.

[32]  Roland Kuhn,et al.  Speaker identification and verification using eigenvoices , 2000, INTERSPEECH.